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Assessment of Child Anthropometry in a Large Epidemiologic Study
09:36

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Published on: February 2, 2017

Validation and refinement of an Australian customised birthweight model using routinely collected data.

Kristen Gibbons1, Allan Chang, Vicki Flenady

  • 1Mater Mothers' Research Centre, Mater Health Services, South Brisbane, Queensland, Australia. kristen.gibbons@mater.org.au

The Australian & New Zealand Journal of Obstetrics & Gynaecology
|December 8, 2010
PubMed
Summary

This study validates and refines a customized birthweight model, finding it stable over time. Updated coefficients improve model performance, offering a more accurate tool for assessing fetal growth.

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Area of Science:

  • Perinatal Medicine
  • Biostatistics
  • Maternal-Fetal Medicine

Background:

  • Customized birthweight models require validation in independent populations.
  • Previous models have not been independently verified, limiting their generalizability.

Purpose of the Study:

  • To validate a previously developed customized birthweight model using new data.
  • To revise and improve the existing model with a larger, more refined dataset.

Main Methods:

  • Model validation using shrinkage statistics on a subset of data (July 2005-December 2008).
  • Model revision via stepwise multiple regression on an expanded dataset (January 1997-December 2008).
  • Performance assessment using individualized birthweight ratios and absolute differences.

Main Results:

  • The existing model coefficients demonstrated stability with minimal shrinkage (<1%).
  • An updated model incorporating refined ethnicity and a smoking term (n=61,630 births) showed improved statistical performance.
  • The updated model achieved a multiple correlation coefficient of 0.51.

Conclusions:

  • The customized birthweight model demonstrates temporal stability within the same hospital setting.
  • While coefficients may be similar across different geographic locations, further formal assessment is needed.
  • The revised coefficients offer superior performance due to dataset enhancements, including refined ethnicity and smoking data.